TY - GEN
T1 - Small object detection with random decision forests
AU - Ma, Juanjuan
AU - Pan, Quan
AU - Hu, Jinwen
AU - Zhao, Chunhui
AU - Guo, Yaning
AU - Wang, Dong
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - The random decision forests method is proposed to detect small object such as UAVs and aircrafts when they occupy a small portion of the field of view, with complex backgrounds, and are filmed by a camera that itself moves. The random decision forests is learned with discriminative decision trees, where every tree internal node is a discriminative classifier. The experimental results show that this small object detection approach achieves good object detection results.
AB - The random decision forests method is proposed to detect small object such as UAVs and aircrafts when they occupy a small portion of the field of view, with complex backgrounds, and are filmed by a camera that itself moves. The random decision forests is learned with discriminative decision trees, where every tree internal node is a discriminative classifier. The experimental results show that this small object detection approach achieves good object detection results.
KW - random decision forests
KW - small object detection
KW - UAV
UR - http://www.scopus.com/inward/record.url?scp=85050869988&partnerID=8YFLogxK
U2 - 10.1109/ICUS.2017.8278409
DO - 10.1109/ICUS.2017.8278409
M3 - 会议稿件
AN - SCOPUS:85050869988
T3 - Proceedings of 2017 IEEE International Conference on Unmanned Systems, ICUS 2017
SP - 566
EP - 571
BT - Proceedings of 2017 IEEE International Conference on Unmanned Systems, ICUS 2017
A2 - Xu, Xin
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Unmanned Systems, ICUS 2017
Y2 - 27 October 2017 through 29 October 2017
ER -